After decades of research and development, the WSR-88D (NEXRAD) network in the United States was upgraded with dual-polarization capability, providing polarimetric radar data (PRD) that have the potential to improve weather observations, quantification, forecasting, and warnings. The weather radar networks in China and other countries are also being upgraded with dual-polarization capability. Now, with radar polarimetry technology having matured, and PRD available both nationally and globally, it is important to understand the current status and future challenges and opportunities. The potential impact of PRD has been limited by their oftentimes subjective and empirical use. More importantly, the community has not begun to regularly derive from PRD the state parameters, such as water mixing ratios and number concentrations, used in numerical weather prediction (NWP) models. In this review, we summarize the current status of weather radar polarimetry, discuss the issues and limitations of PRD usage, and explore potential approaches to more efficiently use PRD for quantitative precipitation estimation and forecasting based on statistical retrieval with physical constraints where prior information is used and observation error is included. This approach aligns the observation-based retrievals favored by the radar meteorology community with the model-based analysis of the NWP community. We also examine the challenges and opportunities of polarimetric phased array radar research and development for future weather observation.
This study examines associations between California Central Valley (CCV) heat waves and the Madden Julian Oscillation (MJO). These heat waves have major economic impact. Our prior work showed that CCV heat waves are frequently preceded by convection over the tropical Indian and eastern Pacific oceans, in patterns identifiable with MJO phases. The main analysis method is lagged composites (formed after each MJO phase pair) of CCV synoptic station temperature, outgoing longwave radiation (OLR), and velocity potential (VP). Over the CCV, positive temperature anomalies occur only after the Indian Ocean (phases 2-3) or eastern Pacific Ocean (phases 8-1) convection (implied by OLR and VP fields). The largest fractions of CCV hot days occur in the two weeks after onset of those two phase pairs. OLR and VP composites have significant subsidence and convergence above divergence over the CCV during heat waves, and these structures are each part of larger patterns having significant areas over the Indian and Pacific Oceans. Prior studies showed that CCV heat waves can be roughly grouped into two clusters: Cluster 2 is preceded by a heat wave over northwestern North America, while Cluster 1 is not. OLR and VP composite analyses are applied separately to these two clusters. However, for Cluster 2, the subsidence and VP over the CCV are not significant, and the large-scale VP pattern has low correlation with the MJO lagged composite field. Therefore, the association between the MJO convection and subsequent CCV heat wave is more evident in Cluster 1 than Cluster 2.
The CarbonTracker (CT) model has been used in previous studies for understanding and predicting the sources, sinks, and dynamics that govern the distribution of atmospheric CO2 at varying ranges of spatial and temporal scales. However, there are still challenges for reproducing accurate model-simulated CO2 concentrations close to the surface, typically associated with high spatial heterogeneity and land cover. In the present study, we evaluated the performance of nested-grid CT model simulations of CO2 based on the CT2016 version through comparison with in-situ observations over East Asia covering the period 2009-13. We selected sites located in coastal, remote, inland, and mountain areas. The results are presented at diurnal and seasonal time periods. At target stations, model agreement with in-situ observations was varied in capturing the diurnal cycle. Overall, biases were less than 6.3 ppm on an all-hourly mean basis, and this was further reduced to a maximum of 4.6 ppm when considering only the daytime. For instance, at Anmyeondo, a small bias was obtained in winter, on the order of 0.2 ppm. The model revealed a diurnal amplitude of CO2 that was nearly flat in winter at Gosan and Anmyeondo stations, while slightly overestimated in the summertime. The model's performance in reproducing the diurnal cycle remains a challenge and requires improvement. The model showed better agreement with the observations in capturing the seasonal variations of CO2 during daytime at most sites, with a correlation coefficient ranging from 0.70 to 0.99. Also, model biases were within -0.3 and 1.3 ppm, except for inland stations (7.7 ppm).
The East Asian westerly jet (EAJ), an important midlatitude circulation of the East Asian summer monsoon system, plays a crucial role in affecting summer rainfall over East Asia. The multimodel ensemble of current coupled models can generally capture the intensity and location of the climatological summer EAJ. However, individual models still exhibit large discrepancies. This study investigates the intermodel diversity in the longitudinal location of the simulated summer EAJ climatology in the present-day climate and its implications for rainfall over East Asia based on 20 CMIP5 models. The results show that the zonal location of the simulated EAJ core is located over either the midlatitude Asian continent or the western North Pacific (WNP) in different models. The zonal shift of the EAJ core depicts a major intermodel diversity of the simulated EAJ climatology. The westward retreat of the EAJ core is related to a warmer mid-upper tropospheric temperature in the midlatitudes, with a southwest-northeast tilt extending from Southwest Asia to Northeast Asia and the northern North Pacific, induced partially by the simulated stronger rainfall climatology over South Asia. The zonal shift of the EAJ core has some implications for the summer rainfall climatology, with stronger rainfall over the East Asian continent and weaker rainfall over the subtropical WNP in relation to the westward-located EAJ core.
In the South China Sea, sea fog brings severe disasters every year, but forecasters have yet to implement an effective sea-fog forecast. To address this issue, we test a liquid-water-content-only (LWC-only) operational sea-fog prediction method based on a regional mesoscale numerical model with a horizontal resolution of about 3 km, the Global and Regional Assimilation and Prediction System (GRAPES), hereafter GRAPES-3km. GRAPES-3km models the LWC over the sea, from which we infer the visibility that is then used to identify fog. We test the GRAPES-3km here against measurements in 2016 and 2017 from coastal-station observations, as well as from buoy data, data from the Integrated Observation Platform for Marine Meteorology, and retrieved fog and cloud patterns from Himawari-8 satellite data. For two cases that we examine in detail, the forecast region of sea fog overlaps well with the multi-observational data within 72 h. Considering forecasting for 0-24 h, GRAPES-3km has a 2-year-average equitable threat score (ETS) of 0.20 and a Heidke skill score (HSS) of 0.335, which is about 5.6% (ETS) and 6.4% (HSS) better than our previous method (GRAPES-MOS). Moreover, the stations near the particularly foggy region around the Leizhou Peninsula have relatively high forecast scores compared to other sea areas. Overall, the results show that GRAPES-3km can roughly predict the formation, evolution, and dissipation of sea fog on the southern China coast.
A new hybrid coupled model (HCM) is presented in this study, which consists of an intermediate tropical Pacific Ocean model and a global atmospheric general circulation model. The ocean component is the intermediate ocean model (IOM) of the intermediate coupled model (ICM) used at the Institute of Oceanology, Chinese Academy of Sciences (IOCAS). The atmospheric component is ECHAM5, the fifth version of the Max Planck Institute for Meteorology atmospheric general circulation model. The HCM integrates its atmospheric and oceanic components by using an anomaly coupling strategy. A 100-year simulation has been made with the HCM and its simulation skills are evaluated, including the interannual variability of SST over the tropical Pacific and the ENSO-related responses of the global atmosphere. The model shows irregular occurrence of ENSO events with a spectral range between two and five years. The amplitude and lifetime of ENSO events and the annual phase-locking of SST anomalies are also reproduced realistically. Despite the slightly stronger variance of SST anomalies over the central Pacific than observed in the HCM, the patterns of atmospheric anomalies related to ENSO, such as sea level pressure, temperature and precipitation, are in broad agreement with observations. Therefore, this model can not only simulate the ENSO variability, but also reproduce the global atmospheric variability associated with ENSO, thereby providing a useful modeling tool for ENSO studies. Further model applications of ENSO modulations by ocean-atmosphere processes, and of ENSO-related climate prediction, are also discussed.
Using GFDL CM2p1 (Geophysical Fluid Dynamics Laboratory Climate Model, version 2p1), the effects of initial sea temperature errors on the predictability of the Indian Ocean Dipole (IOD) are explored. When initial temperature errors are superimposed on the tropical Indian Ocean, a winter predictability barrier (WPB) and a summer predictability barrier (SPB) exist in IOD predictions. The existence of the WPB has a close relation with El Ni?o-Southern Oscillation (ENSO) in the winter of the growing phase of positive IOD events. That is, when ENSO exists in winter, no WPB appears in IOD predictions, and vice versa. In contrast, there is no inherent connection between the existence of the SPB and ENSO. Only the dominant spatial pattern of SPB-related initial errors is studied in this paper, which presents a significant west-east dipole pattern in the tropical Indian Ocean and is similar to that of WPB-related initial errors in previous studies. The SPB-related initial errors superimposed on the tropical Indian Ocean induce the sea surface temperature (SST) and wind anomalies in the tropical Pacific Ocean. Then, under the interaction between the Indian and Pacific oceans through the atmospheric bridge and Indonesian Throughflow, a west-east dipole pattern of SST errors appears in summer, which is further strengthened under the Bjerknes feedback and yields a significant SPB.
In this work, two types of predictability are proposed——forward and backward predictability——and then applied in the nonlinear local Lyapunov exponent approach to the Lorenz63 and Lorenz96 models to quantitatively estimate the local forward and backward predictability limits of states in phase space. The forward predictability mainly focuses on the forward evolution of initial errors superposed on the initial state over time, while the backward predictability is mainly concerned with when the given state can be predicted before this state happens. From the results, there is a negative correlation between the local forward and backward predictability limits. That is, the forward predictability limits are higher when the backward predictability limits are lower, and vice versa. We also find that the sum of forward and backward predictability limits of each state tends to fluctuate around the average value of sums of the forward and backward predictability limits of sufficient states. Furthermore, the average value is constant when the states are sufficient. For different chaotic systems, the average value is dependent on the chaotic systems and more complex chaotic systems get a lower average value. For a single chaotic system, the average value depends on the magnitude of initial perturbations. The average values decrease as the magnitudes of initial perturbations increase.